There are a number of reasons why it is desirable to study the simultaneous responses of many cells at a single site in the mammalian neocortex. Such studies can determine the relative response fields and connectivity of nearby cells, yielding insight into local circuits and the origin of cortical response properties. Such studies can also test the idea that neural information may be encoded in synchronization or correlation of cell responses. Such insights into cortical function are fundamental to our understanding of normal mental function and its pathologies. The purpose of the present proposal is to develop accurate, automatic, quantifiable, objective, and real-time means of discriminating many cells in extracellular recording at a single site. This will be done by combining two methods, each of which has recently achieved significant progress on this discrimination problem: the tetrode method of recording, and Bayesian statistical methods of inferring the distinct waveforms underlying a recording. The tetrode method provides additional information, relative to single-electrode recording, which simplifies discrimination; while the Bayesian methods provide optimal and quantifiable discrimination based on the full information available in the recording. The combination of the two methods will produce a method that can discriminate 5-10 cells at a single site in a quantifiable, reproducible, and largely automatic manner, opening to a new quantity and quality of extracellular recording in the cerebral cortex.